The detection and reconstruction of gravitational waves from core-collapse supernovae (CCSN) present significant challenges due to the highly stochastic nature of the signals and the complexity of detector noise. In this work, we introduce a deep learning framework utilizing a ResNet-50 encoder pre-trained via supervised contrastive learning to classify CCSN signals and distinguish them from instrumental noise artifacts. Our approach explicitly optimizes the feature space to maximize intra-class compactness and inter-class separability. Using a simulated four-detector network (LIGO Hanford, LIGO Livingston, Virgo, and KAGRA) and realistic datasets injecting magnetorotational and neutrino-driven waveforms, we demonstrate that the contrastive learning paradigm establishes a superior metric structure within the embedding space, significantly enhancing detection efficiency. At a false positive rate of 10−4, our method achieves a true positive rate (TPR) of nearly 100% for both rotational and neutrino-driven signals within a distance range of 10--200~kpc, while maintaining a TPR of approximately 80% at 1200~kpc. In contrast, traditional end-to-end methods yield a TPR below 20% for rotational signals at distances ≥200~kpc, and fail to exceed 60% for neutrino-driven signals even at a close proximity of 10~kpc.
@article{arxiv.2601.01376,
title = {Classifying Core-Collapse Supernova Gravitational Waves using Supervised Contrastive Learning},
author = {Ao-Bo Wang and Yong Yuan and Hao Cai and Xi-Long Fan},
journal= {arXiv preprint arXiv:2601.01376},
year = {2026}
}